code: def test_tf_coh_criterion_forward(input_ch):
      criterion = FrequencyDomainAbsCoherence()
      complex_wrapper = (
          torch.complex if is_torch_1_9_plus else ComplexTensor
      )
  
      batch = 2
      shape = (
          (batch, 10, 200)
          if input_ch == 1
          else (batch, 10, input_ch, 200)
      )
      inf_spec = complex_wrapper(torch.rand(*shape), torch.rand(*shape))
      ref_spec = complex_wrapper(torch.rand(*shape), torch.rand(*shape))
  
      loss = criterion(ref_spec, inf_spec)
      assert loss.shape == (batch,), (
          "Invlid loss shape with " + criterion.name
      )
  
file_path: espnet/test/espnet2/enh/loss/criterions/test_tf_domain.py
imports: import pytest
  import torch
  from packaging.version import parse
  from torch_complex import ComplexTensor
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainAbsCoherence, FrequencyDomainCrossEntropy, FrequencyDomainDPCL, FrequencyDomainL1, FrequencyDomainMSE
label_desc: Use image node_id 2 for calling a global function with example usage: test_tf_coh_criterion_forward(input_ch) without return types